Founder & Engineer / Applied AI
I design the systems around models: how they receive context, route work, remember state, use tools, pass checks, and act inside real products and workflows.
AI is one specialized capability within a broader engineering practice. It works best when product, software, and operations are designed together.
Understand
Context and retrieval
Decide
Routing and reasoning
Act
Tools and workflows
Verify
Evaluation and checks
System / 01
Useful behavior comes from the control system around a model. Each layer narrows uncertainty, supplies capability, or makes an outcome easier to inspect.
Instructions, retrieved knowledge, task state, and relevant memory.
The right model or deterministic path for the task, cost, and risk.
Bounded reasoning, generation, classification, or extraction.
Typed access to services, data, and actions outside the model.
Sequencing, retries, approvals, state, and human handoffs.
Structure, evidence, policy, and outcome validation before action.
Models can change. The surrounding contracts should remain legible, testable, and observable.
Method / 02
A prototype proves that a behavior is possible. The next step is making that behavior bounded, testable, observable, and useful under real operating constraints.
01
Define the job, the source of truth, the decisions a model may influence, and what an unacceptable failure looks like.
Boundaries / Risk / Success criteria
02
Separate model judgment from deterministic logic, then make context, routing, tools, memory, and state explicit.
Contracts / State / Tool access
03
Test representative cases, inspect traces, and combine model-based evaluation with checks that can be asserted directly.
Test sets / Traces / Review
04
Instrument quality, latency, cost, and failure paths so the system can improve from observed behavior rather than intuition.
Observability / Fallbacks / Iteration
Technical examples / 03
These are system patterns, not chatbot skins. Each combines probabilistic model behavior with deterministic controls and a clear operational outcome.
01
Question -> retrieval -> source checks -> synthesis
An agent works from bounded sources, selects tools, records evidence, and produces an answer that can be inspected instead of merely trusted.
Useful property
Traceable answers
02
Event -> classification -> approval -> tool action
A model classifies or proposes the next step while workflow state, permissions, approvals, and deterministic services control what happens.
Useful property
Controlled actions
03
Product state -> model -> validation -> fallback
An AI feature receives relevant product context, returns a typed response, and follows explicit validation, fallback, latency, and telemetry contracts.
Useful property
Predictable behavior
Collaboration / 04
For applied AI products, agents, and automation where architecture and execution matter as much as model choice.